Method and apparatus for providing a binary fingerprint image

ABSTRACT

Embodiments of a method and/or apparatus to provide a binary image are disclosed.

RELATED APPLICATIONS

[0001] This patent application is related to concurrently filed U.S.patent application Ser. No. ______, (attorney docket 042390.P12798)titled “Method and Apparatus to Reduce False Minutiae from a BinaryFingerprint Image,” filed on ______, by Acharya et al., and toconcurrently filed U.S. patent application Ser. No. ______, (attorneydocket 042390.P12877) titled “Architecture for Processing FingerprintImages,” filed on ______, by Acharya et al., both assigned to theassignee of the presently claimed subject matter and herein incorporatedby reference.

BACKGROUND

[0002] This disclosure is related to classification and featureextraction.

[0003] Feature extraction is a current area of research and developmentin digital image processing and computer vision, particularly in areasof development involving feature based pattern recognition. Many imagerecognition, image detection, and biometrics applications, for example,have been developed based on techniques of feature extraction andpattern recognition. Feature extraction in fingerprint images has uniqueaspects compared to general purpose image processing applications atleast in part due to its special topological characteristics. Most ofthe approaches proposed in the literature transform a fingerprint imageinto a binary image proposed in the literature transform a fingerprintimage into a binary image based at least in part on convolution of theimage with a filter coupled with certain variants of thresholding.However, this approach has several disadvantages, such as computationalintensity and the inability to robustly address noisy images. A need,therefore, exists for other processing techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Subject matter is particularly pointed out and distinctly claimedin the concluding portion of the specification. The claimed subjectmatter, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference of the following detailed description when readwith the accompanying drawings in which:

[0005]FIG. 1 is a perspective diagram of a gray-scale topology for afinger print image;

[0006]FIG. 2 is a schematic diagram of a pixel and its surrounding 8pixel neighborhood;

[0007]FIG. 3 is a table providing nine gray-scale topologicalpossibilities for the directions (a, b), (c, d), (e, f), and (g, h), asshown in the neighborhood of FIG. 2;

[0008]FIG. 4 is a table providing classifications of a pixel for anembodiment described herein;

[0009]FIG. 5 is another table depicting relationships betweenpreliminary and final pixel classifications for an embodiment describedherein; and

[0010] FIGS. 6(a), 6(b), and 6(c) are fingerprint images illustratingapplication of an embodiment described herein.

DETAILED DESCRIPTION

[0011] In the following detailed description, numerous specific detailsare set forth in order to provide a thorough understanding of theclaimed subject matter. However, it will be understood by those skilledin the art that the claimed subject matter may be practiced withoutthese specific details. In other instances, well-known methods,procedures, components and circuits have not been described in detail inorder so as not to obscure the claimed subject matter.

[0012] In one embodiment of the claimed subject matter, a technique ofclassifying a pixel may be based, at least in part, on its gray-scaletopological relationship with neighboring pixels in a fingerprint image.In this particular embodiment, the pixel may be classified into one offour classes. These four classes, in this embodiment, are called crest,valley, plateau and undecided. Classification embodiments in accordancewith the claimed subject matter may be further exploited to extractunique features of a gray-scale fingerprint image suitable for automaticfingerprint identification system (AFIS), as described, for example, inaforementioned concurrently filed U.S. patent application Ser. No.______ (attorney docket no. 042390.P12798). Of course, the claimedsubject matter is not limited in scope to being employed in this manner.This is just one example of a potential application of the claimedsubject matter.

[0013] Fingerprint images have several unique topological features thatmay be applied for fingerprint recognition and identification. In afingerprint image, there are curved contours, referred to in thiscontext as ridge lines. Ridge lines correspond to minute elevations onthe skin of the finger, that may, for example, end abruptly or splitinto two or more ridges. The points at which ridges end or split may beunique characteristics of a fingerprint and are called “minutiae” or“Galton characteristics” according to its observer, Sir Francis Galton.See, for example, F. Galton, Fingerprints, London: Macmillan, 1892. Asis well-known, by correlating minutiae sets, an expert may matchfingerprints. Several AFIS utilize minutiae matching techniques. See,for example, B. M. Metre, Fingerprint Image Analysis for AutomaticIdentification, Machine Vision and Applications, vol. 6, no. 2, pp.124-139, 1993; V. S. Srinivasan and N. N. Murthy, Detection of SingularPoints in Fingerprint Images, Pattern Recognition, vol. 25, no. 2, pp.139-153, 1992; J. Hollingum, Automated Fingerprint Analysis Offers FastVerification, Sensor Review, vol. 12, no. 13, pp. 12-15, 1992; B. M.Metre and B. Chatterjee, Segmentation of Fingerprint Images—a CompositeMethod, Pattern Recognition, vol. 22, no. 4, pp. 381-385, 1989; B. M.Mehtre and N. N. Murthy, A Minutia Based Fingerprint IdentificationSystem, in the Proceedings, Second International Conference on Advancesin Pattern Recognition and Digital Techniques, Calcutta, 1986. Methodsfor pre-processing of fingerprint images are described in theliterature, e.g., L. O'Gorman and J. V. Nickerson, An approach toFingerprint Filter Design, Pattern Recognition, vol. 22, no. 1, pp.29-38, 1989. However, these methods are typically slow, complicated,and/or difficult to implement in hardware. Furthermore, the process ofautomatic detection of minutiae may become more difficult when thequality of a fingerprint image degrades, such as due at least in part tonoise.

[0014] In this particular embodiment, the pixels in a fingerprint imagemay be classified as described in more detail hereinafter. Let I be an(m×n) gray-scale image with g gray levels. Let gray(i, j) be the graylevel of the (i, j)-th pixel of I. This is denoted as P(i, j), wherei=0, 1, . . . , m−1, and where j=0, 1, 2, . . . , n−1. An example of adiscrete surface z=gray(i, j) that may correspond to such an image I isshown in FIG. 1. By associating bright pixels with gray levels close tog−1 and dark pixels with gray levels close to zero, the fingerprintridge lines, appearing bright in image I, may correspond to surfaceridges or crests, and the space between ridge lines, appearing dark inI, may correspond to ravines or valleys, as described in more detailhereinafter.

[0015] Consider, for example, an eight-pixel neighborhood of P(i, j), inthis particular embodiment.

[0016] Let, a=gray(i, j)−gray(i−1, j),

[0017] b=gray(i+1, j)−gray(i, j),

[0018] c=gray(i, j)−gray(i−1, j−1),

[0019] d=gray(i+1, j+1)−gray(i, j),

[0020] e=gray(i, j)−gray(i, j−1),

[0021] f=gray(i, j+1)−gray(i, j),

[0022] g=gray(i, j)−gray(i+1, j−1),

[0023] h=gray(i−1, j+1)−gray(i, j).

[0024] The relative gray-scale topological configuration of P in itslocality may be be viewed from four possible directions as shown in FIG.2. These are:

[0025] (a, b): from north to south,

[0026] (c, d): from north-west to south-east,

[0027] (e, f): from west to east, and,

[0028] (g, h): from south-west to north-east.

[0029] Furthermore, the eight parameters (a, b, c, d, e, f, g, h) may bepositive (increasing gray-scale gradient), negative (decreasinggray-scale gradient), or substantially zero (substantially constantgray-scale gradient), and, therefore, may give rise to three cases. Aconsideration of the pair-wise properties of the eight parametersindicates that a pair of parameters may have nine (9) cases as laid outin FIG. 3.

[0030] For (a, b), consider P=(i, j), P′=(i−1, j), P″=(i+1, j).

[0031] For (c, d), consider P=(i, j), P′=(i−1, j−1), P″=(i+1, j+1).

[0032] For (e, f), consider P=(i, j), P′=(i, j−1), P″=(i, j+1).

[0033] For (g, h), consider P=(i, j), P′=(i+1, j−1), P″=(i−1, j+1).

[0034] The fourth column in FIG. 3, named “Gray Levels,” exhibits thepictorial representation of the three points P, P′, P″, where P′ and P″are the adjacent pixels of P in the concerned pair of parameters. Theheight of the point P in the pictorial representation, in FIG. 3,indicates the relative position of P in the gray-scale topology withrespect to its two neighbors P′ and P″.

[0035] Now, in FIG. 2, for any pixel P(i, j) in the image I, evaluatingthe values of the four pairs of parameters indicates the topologicalconfiguration of P in its locality. As shown in the right column of FIG.3, the value of the four pairs of parameters ((a, b), (c, d), (e, f),(g, h)) is used to assign one out of the following four classes to therespective pixel P:

[0036] (i) CR: P is a crest pixel if either the gray value of P ishigher than the gray values of both P′ and P″ (e.g., case 2 in FIG. 3),or, the gray value of P is the same or nearly the same as that of one ofP′ and P″, but higher than that of the other (e.g., case 3 and case 8).

[0037] (ii) VA: P is a valley pixel if either the gray value of P islower than the gray values of both P′ and P″ (e.g., case 4), or, thegray value of P is the same or nearly the same as that of one of P′ andP″ but lower than that of the other (e.g., case 6 and case 7).

[0038] (iii) PL: P is a plateau pixel if the gray value of P liesstrictly within the gray values of P′ and P″ (e.g., case 1 and case 5).

[0039] (iv) UN: P is an undecidable pixel if the gray values of P, P′and P″ are the same or nearly the same (e.g., case 9).

[0040] Thus, in this embodiment, a pixel P is assigned to one of fourpreliminary classes depending, at least in part, on its relativetopological position, guided by the values of the four parameter pairsalong four directions, namely, north to south, north-west to south-east,east to west, and south-west to north-east. After this preliminaryclassification is made, P may be either strongly classified or weaklyclassified, as described in more detail hereinafter.

[0041] Let C_(ab), C_(cd), C_(ef) and C_(gh) denote the four classespreliminarily assigned to P by the four pairs of parameters ((a, b), (c,d), (e, f), (g, h)). Note that these four classes are one of the fourpossible preliminary classes (CR, VA, PL and UN). In this particularembodiment, P is strongly classified to one of the classes among CR, VAand PL if three or four classes among C_(ab), C_(cd), C_(ef) and C_(gh)are the same. For instance, if the four classes preliminarily attachedwith P are {CR, PL, CR, CR}, then P is strongly classified as a crest(CR). Citing another, if P has a preliminary classification {VA, VA, UN,VA}, then P is strongly classified as a valley (VA). But, if P has got{UN, UN, UN, CR}, or has {CR, VA, CR, PL}, then P is not stronglyclassified in this embodiment. Therefore, P is said to be weaklyclassified when it fails to satisfy the criterion of strongclassification. The table in FIG. 4 elucidates the possibilities ofpreliminary (first-pass) classification which is followed by a second orfinal classification of P, as described in more detail hereinafter, forthis embodiment.

[0042] In FIG. 5, the semi-final or preliminary classes CR, VA and PLare effectively the final classes, for this particular embodiment. Notethat the criteria of being strongly classified, mentioned previously, issatisfied by some of the cases in the table in FIG. 4, which aredesignated by unambiguous classes CR, VA, or PL. Pixels that areclassified strongly in the first pass are treated as final in thisparticular embodiment.

[0043] For pixels not able to be unambiguously classified based, atleast in part, on, for a particular pixel, at least some or all of theimmediately adjacent pixels, the pixel is classified, in this particularembodiment, based, at least in part, on its gray-level value and therelationship of its gray-level value to the average gray-level value ofthe pixels that have been classified. Thus, the pixels, which areclassified as a remaining class, here four ambiguous, preliminaryclasses (CV, CP, VP, XX) in this particular embodiment, are finallyclassified in the second pass, although, of course, the claimed subjectmatter is not limited in scope in this respect.

[0044] As is evident in FIG. 5, there may be four cases in thisparticular embodiment that cover the ambiguous classes. The implicationsof these ambiguous classes are indicated in the second column of thetable of FIG. 5. In the second pass, from the unambiguously classifiedpixels in the first pass, the average gray values of three of theclasses may be estimated. For example, in this embodiment, CR (crest orridge), VA (valley or ravine), and PL (plateau which is neither crestnor valley) may be employed in this manner, as described in more detailhereinafter. These three average gray values may then be used to resolvethe ambiguity of classification in the second pass. One possibletechnique, although, of course, the claimed subject matter is notlimited in scope in this respect, the ambiguously classified pixel P maybe classified unambiguously depending at least in part on the proximityof its gray value with the gray values for the three classes. For thisembodiment, for example, P is classified into the class with which itsgray value difference is smallest, or, to which its gray value isclosest.

[0045] Below is provided a pseudo-code implementation of the previouslydescribed embodiment. Again, this implementation is provided merely asone possible embodiment within the scope of the claimed subject matterand is not intended to limit the scope of the appended claims. ProcessA1: Preliminary Classification Input: 1. original image (image_(ori));2.   image height (m); 3.   image width (n); Output: intermediate image(image_(int1)); Steps: 1. for (i = 1 to m−2 by 1) 2. for (j = 1 to n−2by 1) 3. evaluate the 8 parameters {a, b, c, . . ., g, h}; 4. evaluate{C_(ab), C_(cd), C_(ef), C_(gh)}; 5. find the preliminary class C_(pre)from Fig. 4; 6. assign C_(pre) to image_(int1)[i][j]; 7. end for 8. endfor Process A2: Final Classification Input: 1. original image(image_(ori)); 2. intermediate image (image_(int1)); 3. image height(m); 4. image width (n); Output: image (image_(int2)); Steps: 1.initialize the average gray values of unambiguous classes: CR_(avg) = 0,VA_(avg) = 0, PL_(avg) = 0; 2. for (i = 1 to m−2 by 1) 3. for (j = 1 ton−2 by 1) 4. if image_(int1)[i][j] is equal to CR 5. update CR_(avg); 6.else 7. if image_(int1)[i][j] is equal to VA 8. update VA_(avg); 9. else10. if image_(int1)[i][j] is equal to PL 11. update PL_(avg); 12. endfor 13. end for 14. for (i = 1 to m−2 by 1) 15. for (j = 1 to n−2 by 1)16. if image_(int1)[i][j] is equal to CV or CP or VP or XX 17. find theclass C_(un) from {CR, VA, PL} such that C_(un) has least gray valuedifference from image_(int1)[i][j]; 18. assign gray value of C_(un) toimage_(int1)[i][j]; 19. end for 20. end for 21. rename image_(int1) asimage_(int2);

[0046] Application of this particular implementation to a fingerprintimage may provide an image_(int2) which contains 2 or 3 pixels thickridge or crest lines, ravine or valley lines, and the rest plateauregions. It has been experimentally observed that gray scale fingerprintimages may yield such results when this particular embodiment isapplied, although in some cases pixel width may vary. It may bedesirable to then thin image_(int2) so that the ridge lines areone-pixel thick to represent the edges in the fingerprint image.Although the claimed subject matter is not limited in scope in thisrespect, a standard thinning technique, such as described, for example,in A. Rosenfield, A. C. Kak, Digital Image Processing, vol. 2, AcademicPress Inc., Orlando, Fla., 1982, or in L. O'Gorman, k×k Thinning,Computer Vision, Graphics and Image Processing, pp. 195-215, 1990, maybe applied. It is, of course, understood that any one of a number ofpossible techniques may be applied to accomplish this result and theclaimed subject matter is not limited in scope to any particulartechnique.

[0047] After thinning ridge lines in image_(int2), image_(int3) isproduced which contains one-pixel thick ridge lines on a substantiallyuniform background. The ravines/valleys and the plateau regions aregenerally of use no longer as the minutiae are located on the crestlines and, therefore, in image_(int3), these features are not retained.Thus, image_(int3) is effectively a binary image where the ridge linesor crests have a substantially uniform gray value of 1 (object, here),and other pixels have a substantially uniformly gray value of 0(background, here). Hence, this embodiment provides a binary fingerprintimage. FIGS. 6(a),(b), and (c) provide sample fingerprint images thatexhibit the different stages of processing of the gray-scale image.

[0048] The previously described embodiment provides a number ofpotential advantages, although, the claimed subject matter is notlimited to the specific embodiment described or to the associatedadvantages. For example, as previously described, application of thepreviously described approach and variations thereof may be employed toextract topological features and produce a binary image from agray-scale fingerprint image. This binary image may then be used forfingerprint analysis. Likewise, such a process is robust. Experimentalresults on the Special Database-14 of the National Institute ofStandards and Technology (NIST), Gaithersburg, Md. 20899, USA show thatthe features may be extracted from noisy fingerprint images as well. Forexample, a noisy fingerprint gray tone image as in FIG. 6(a), yields aone-pixel-wide binary image as in FIG. 6(c). Likewise, such a techniqueand variants thereof may be extended to other applications and types ofimages. For example, it may be applied to edge detection for generalimage processing applications.

[0049] It will, of course, be understood that, although particularembodiments have just been described, the claimed subject matter is notlimited in scope to a particular embodiment or implementation. Forexample, one or more of the processing embodiments described may beimplemented in hardware, such as in an integrated circuit that processesa gray-scale fingerprint image, whereas another embodiment may, instead,be implemented in software. Likewise, an embodiment may be in firmware,or any combination of hardware, software, or firmware, for example.Likewise, although the claimed subject matter is not limited in scope inthis respect, one embodiment may comprise an article, such as a storagemedium. Such a storage medium, such as, for example, a CD-ROM, or adisk, may have stored thereon instructions, which, when executed by asystem, such as a computer system or platform, or a imaging orfingerprint image system, for example, may result in an embodiment of amethod in accordance with the claimed subject matter being executed,such as an embodiment of a method of providing a binary fingerprintimage, for example, as previously described. For example, an imageprocessing platform or an image processing system may include an imageprocessing unit, an image input/output device and/or memory.

[0050] While certain features of the claimed subject matter have beenillustrated and described herein, many modifications, substitutions,changes and equivalents will now occur to those skilled in the art. Itis, therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the claimed subject matter.

1. A method of producing a binary fingerprint image from a gray-scaleimage, said method comprising: classifying pixels based, at least inpart, on, for a particular pixel, the gray-level topologicalrelationship with neighboring pixels.
 2. The method of claim 1, whereinclassifying pixels based, at least in part, on, for a particular pixel,the gray-level topological relationship with neighboring pixelscomprises: classifying pixels based, at least in part, on, for aparticular pixel, the gradient of the gray-level with at least someimmediately adjacent pixels.
 3. The method of claim 2, wherein thepixels are classified into one of multiple classes, based, at least inpart, on, for a particular pixel, the gradient of the gray-level with atleast some immediately adjacent pixels.
 4. The method of claim 3,wherein the pixels are classified into one of at least four classes,based, at least in part, on for a particular pixel, the gradient of thegray-level with all immediately adjacent pixels.
 5. The method of claim3, wherein three of the classes comprise: crest, plateau and valley. 6.The method of claim 3, and further comprising: for pixels not able to beunambiguously classified based, at least in part, on, for a particularpixel, at least some immediately adjacent pixels, classifying the pixelbased, at least in part, on its gray-level value and the relationship ofits gray-level value to the average gray-level value of the pixels thathave been classified.
 7. The method of claim 6, and further comprising:thinning any resulting ridge lines to be one pixel thick.
 8. The methodof claim 1, wherein said gray-scale image comprises a gray-scalefingerprint image
 9. An article comprising: a storage medium, saidstorage medium having stored thereon instructions, that, when executed,result in performance of a method of producing a binary image from agray-scale image, said method comprising: classifying pixels based, atleast in part, on, for a particular pixel, the gray-level topologicalrelationship with neighboring pixels.
 10. The article of claim 9,wherein said instructions, when executed, result in classifying pixelsbased, at least in part, on, for a particular pixel, the gray-leveltopological relationship with neighboring pixels in a manner thatfurther comprises classifying pixels based, at least in part, on, for aparticular pixel, the gradient of the gray-level with at least someimmediately adjacent pixels.
 11. The article of claim 10, wherein saidinstructions, when executed, result in classifying pixels based, atleast in part, on, for a particular pixel, the gradient of thegray-level with at least some immediately adjacent pixels in a mannerwherein the pixels are classified into one of multiple classes, based,at least in part, on, for a particular pixel, the gradient of thegray-level with at least some immediately adjacent pixels.
 12. Thearticle of claim 11, wherein said instructions, when executed, result inclassifying pixels in a manner wherein the pixels classified into one ofmultiple classes are classified into one of at least four classes,based, at least in part, on for a particular pixel, the gradient of thegray-level with all immediately adjacent pixels.
 13. The article ofclaim 11, wherein said instructions, when executed, further result in,for pixels not able to be unambiguously classified based, at least inpart, on, for a particular pixel, at least some immediately adjacentpixels, classifying the pixel based, at least in part, on its gray-levelvalue and the relationship of its gray-level value to the averagegray-level value of the pixels that have been classified.
 14. Thearticle of claim 13, wherein said instructions, when executed, furtherresult in thinning any resulting ridge lines to be one pixel thick. 15.The article of claim 9, wherein said instructions, when executed, resultin performance of a method of producing a binary image from a gray-scaleimage,
 16. An apparatus comprising: an integrated circuit; saidintegrated circuit being adapted to classify pixels of a gray-scaleimage based, at least in part, on, for a particular pixel, thegray-level topological relationship with neighboring pixels.
 17. Theapparatus of claim 14, wherein said gray-scale image comprises agray-scale fingerprint image.
 18. The apparatus of claim 16, whereinsaid integrated circuit is further adapted to classify pixels in amanner wherein the pixels are classified into one of at least fourclasses, based, at least in part, on for a particular pixel, thegradient of the gray-level with all immediately adjacent pixels.
 19. Theapparatus of claim 18, wherein said integrate circuit is further adaptedto, for pixels not able to be unambiguously classified based, at leastin part, on, for a particular pixel, all immediately adjacent pixels,classify the pixel based, at least in part, on its gray-level value andthe relationship of its gray-level value to the average gray-level valueof the pixels that have been classified.
 20. The apparatus of claim 19,wherein said integrated circuit is further adapted to thin any resultingridge lines to be one pixel thick.